Version 1
: Received: 11 July 2021 / Approved: 12 July 2021 / Online: 12 July 2021 (14:36:26 CEST)
How to cite:
Nageswaran, C. The Snooker Algorithm for Ultrasonic Imaging of Fatigue Cracks in order to use Parameter-Spaces to Aid Machine Learning. Preprints2021, 2021070269. https://doi.org/10.20944/preprints202107.0269.v1
Nageswaran, C. The Snooker Algorithm for Ultrasonic Imaging of Fatigue Cracks in order to use Parameter-Spaces to Aid Machine Learning. Preprints 2021, 2021070269. https://doi.org/10.20944/preprints202107.0269.v1
Nageswaran, C. The Snooker Algorithm for Ultrasonic Imaging of Fatigue Cracks in order to use Parameter-Spaces to Aid Machine Learning. Preprints2021, 2021070269. https://doi.org/10.20944/preprints202107.0269.v1
APA Style
Nageswaran, C. (2021). The Snooker Algorithm for Ultrasonic Imaging of Fatigue Cracks in order to use Parameter-Spaces to Aid Machine Learning. Preprints. https://doi.org/10.20944/preprints202107.0269.v1
Chicago/Turabian Style
Nageswaran, C. 2021 "The Snooker Algorithm for Ultrasonic Imaging of Fatigue Cracks in order to use Parameter-Spaces to Aid Machine Learning" Preprints. https://doi.org/10.20944/preprints202107.0269.v1
Abstract
Fatigue cracks in a wide array of industrial components and structures pose a significant threat to their integrity. Detecting fatigue cracks using ultrasonic inspection techniques is a widespread activity for economic reasons but there are limitations to the techniques due to the morphology of fatigue cracks. In addition to detection there is a need to measure the size of the cracks which are often within the volume of the material. Ultrasonic techniques are well-suited to look inside the volume of the material but achieving sufficient sensitivity to the tip of the cracks in particular is practically difficult. Without an accurate knowledge of where the tip of the crack lies there can be significant uncertainty in sizing measurements. Machine Learning (ML) techniques are being developed to aid in the inspection and monitoring tasks but presenting the ultrasonic data in a suitable way for machine learning is very important. This paper presents a new approach to condition the ultrasonic data for machine learning settings so that they can be used effectively and confidently to detect and size fatigue cracks. The new approach, using images termed parameter-spaces, will also aid in conventional inspections as they are able to give information to human operators as to the existence or not of these very dangerous cracks.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
